fix: bernoulli_loader honors max_prob kwarg (#743)

bernoulli_loader read kwargs.get("dt") instead of kwargs.get("max_prob").
Since dt is a named parameter it never lands in **kwargs, so max_prob was
silently ignored and the spike rate stayed at 1.0 regardless of the argument.

Adds test_bernoulli_loader_max_prob, which asserts the empirical spike rate
tracks max_prob (the existing test only checked output shape, so the bug went
undetected). Fix mirrors PR #743.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

Author: Hananel-Hazan

bindsnet/encoding/loaders.py

@@ -26,7 +26,7 @@ def bernoulli_loader(
     :param float max_prob: Maximum probability of spike per Bernoulli trial.
     """
     # Setting kwargs.
-    max_prob = kwargs.get("dt", 1.0)
+    max_prob = kwargs.get("max_prob", 1.0)
 
     for i in range(len(data)):
         # Encode datum as Bernoulli spike trains.

test/encoding/test_encoding.py

@@ -37,6 +37,29 @@ def test_bernoulli_loader(self):
                         for i, spikes in enumerate(spike_loader):
                             assert spikes.size() == torch.Size((t, n))
 
+    def test_bernoulli_loader_max_prob(self):
+        # Regression test (PR #743): bernoulli_loader must honor the ``max_prob``
+        # keyword argument. Previously it read ``kwargs.get("dt")``, which never
+        # exists in kwargs (``dt`` is a named parameter), so max_prob was silently
+        # ignored and the spike rate stayed at 1.0 regardless of the argument.
+        torch.manual_seed(0)
+
+        # All-ones input over many trials: the empirical spike rate should track
+        # max_prob, not the dt default of 1.0.
+        data = torch.ones(1, 20000)
+        for m in [0.0, 0.25, 0.5, 0.75]:
+            spikes = next(bernoulli_loader(data, time=1, max_prob=m))
+            assert abs(spikes.float().mean().item() - m) < 0.02
+
+        # dt must not leak into max_prob: with dt set but max_prob explicit,
+        # the rate follows max_prob.
+        spikes = next(bernoulli_loader(data, time=1, dt=0.5, max_prob=0.3))
+        assert abs(spikes.float().mean().item() - 0.3) < 0.02
+
+        # Back-compat: omitting max_prob defaults to 1.0.
+        spikes = next(bernoulli_loader(data, time=1))
+        assert spikes.float().mean().item() == 1.0
+
     def test_poisson(self):
         for n in [1, 100]:  # number of nodes in layer
             for t in [1000]:  # number of timesteps